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HyOPTXGBoost and HyOPTRF: Hybridized Intelligent Systems using Optuna Optimization Framework for Heart Disease Prediction with Clinical Interpretations

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Abstract

Recently, every human being has been busy keeping pace with fast changes and is unable to put his attention on a healthy lifestyle. This unhealthy lifestyle leads to several fatal diseases like heart disease (HD), cancer disease, allergies, asthma, breast cancer, etc. These diseases are increasing the morbidity and mortality rate every year, and out of these HD is more fetal. Earlier/Timely prediction of HD is regarded as one of the most crucial tasks in the medical field. In the healthcare industry, a big collection of datasets is available free of cost. Motivated by existing challenges, the authors proposed two intelligent models i.e., HyOPTRF (Model 1), and HyOPTXGBoost Classifier (Model 2), implemented on the Statlog HD dataset with default and hyper-tuned parameters. Both the models recorded maximum Accuracy of 92.59%, Specificity 100%, Precision 100%, Negative Predicted Value 100%, Geometric mean 93.93% F1 Score 93.75%, Training Time 0.02s, and Testing Time 0.005s for the HyOPTRF on Trial no. 2nd and Accuracy of 96.30%, Specificity 100%, Precision 100%, Negative Predicted Value 100%, Geometric mean 96.82%, F1 Score 96.77%, Training Time 0.009s, and Testing Time 0.002s for the HyOPTXGBoost Classifier on Trial no. 33rd. The proposed models were validated by the Stratify Kfold Cross-Validation technique and compared with the other existing models.

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Data availability

In this research study an open-source dataset has been used, which is freely available on the UCI Machine Learning repository at https://archive.ics.uci.edu/ml/datasets/statlog+(heart).

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Funding

This research study received no external funding.

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Authors and Affiliations

  1. Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, Sangrur, Punjab, India

    Sanjay Dhanka & Surita Maini

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  1. Sanjay Dhanka
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  2. Surita Maini
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Contributions

Sanjay Dhanka: Data curation, Resources, Investigation, Formal analysis, Conceptualization, Methodology, Project administration, Prepared original draft, Review and editing, Validation. Surita Maini: Methodology, Supervision, Project administration, Review and editing.

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Correspondence to Sanjay Dhanka.

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The authors declare that they have no competing interests. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this research study.

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Dhanka, S., Maini, S. HyOPTXGBoost and HyOPTRF: Hybridized Intelligent Systems using Optuna Optimization Framework for Heart Disease Prediction with Clinical Interpretations. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18312-x

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